Introducing collective crisis intelligence

Curated on Posted on by Stefaan Verhulst

Blogpost by Annemarie Poorterman et al: “…It has been estimated that over 600,000 Syrians have been killed since the start of the civil war, including tens of thousands of civilians killed in airstrike attacks. Predicting where and when strikes will occur and issuing time-critical warnings enabling civilians to seek safety is an ongoing challenge. It was this problem that motivated the development of Sentry Syria, an early warning system that alerts citizens to a possible airstrike. Sentry uses acoustic sensor data, reports from on-the-ground volunteers, and open media ‘scraping’ to detect warplanes in flight. It uses historical data and AI to validate the information from these different data sources and then issues warnings to civilians 5-10 minutes in advance of a strike via social media, TV, radio and sirens. These extra minutes can be the difference between life and death.

Sentry Syria is just one example of an emerging approach in the humanitarian response we call collective crisis intelligence (CCI). CCI methods combine the collective intelligence (CI) of local community actors (e.g. volunteer plane spotters in the case of Sentry) with a wide range of additional data sources, artificial intelligence (AI) and predictive analytics to support crisis management and reduce the devastating impacts of humanitarian emergencies….(More)”

Can national statistical offices shape the data revolution?

Curated on Posted on by Stefaan Verhulst

Article by Juan Daniel Oviedo, Katharina Fenz, François Fonteneau, and Simon Riedl: “In recent years, breakthrough technologies in artificial intelligence (AI) and the use of satellite imagery made it possible to disrupt the way we collect, process, and analyze data. Facilitated by the intersection of new statistical techniques and the availability of (big) data, it is now possible to create hypergranular estimates.

National statistical offices (NSOs) could be at the forefront of this change. Conventional tasks of statistical offices, such as the coordination of household surveys and censuses, will remain at the core of their work. However, just like AI can enhance the capabilities of doctors, it also has the potential to make statistical offices better, faster, and eventually cheaper.

Still, many countries struggle to make this happen. In a COVID-19 world marked by constrained financial and statistical capacities, making innovation work for statistical offices is of prime importance to create better lives for all…

In the case of Colombia, this novel method facilitated a scale-up from existing poverty estimates that contained 1,123 data points to 78,000 data points, which represents a 70-fold increase. This results in much more granular estimates highlighting Colombia’s heterogeneity between and within municipalities (see Figure 1).

Figure 1. Poverty shares (%) Colombia, in 2018

Figure 1. Poverty shares (%) Colombia, in 2018

Traditional methods don´t allow for cost-efficient hypergranular estimations but serve as a reference point, due to their ground-truthing capacity. Hence, we have combined existing data with novel AI techniques, to go down to granular estimates of up to 4×4 kilometers. In particular, we have trained an algorithm to connect daytime and nighttime satellite images….(More)”.

Civic Technology for Participatory Cities of the Future

Curated on Posted on by Stefaan Verhulst

Article by Francesca Esses: “As cities continue to expand and demographics diversify, it has become challenging for governments, on a local level, to make informed decisions representative of the local population. Through town hall meetings and public hearings, traditional means of public engagement are no longer sufficient in attaining meaningful citizen input to policy and decision making. These types of engagement methods have come under criticism for their inaccessibility, timelines, and representation of the broader demographic of modern society…

Winston Churchill is famously quoted as saying, “Never let a good crisis go to waste”. History would suggest that pandemics have forced humans to embrace change, described as a ‘portal’ from the old world to the next. COVID-19 has created an unparalleled opportunity to reimagine technology’s role in shaping society. It is anticipated that a surge in technological innovation will materialise from the pandemic and the subsequent economic instability.

Concerning smart cities, the COVID-19 pandemic has been referred to as the “lubricant” for further development in this area. There has been a significant rise in civic tech projects globally as a direct response to the pandemic: organisations such as Code for Japan, Code for Germany, and Code for Pakistan have all launched several projects in response to the virus. We’ve already seen civic tech initiatives across Africa implemented as a direct response to the pandemic; the Civic Tech Innovation Network referenced at least 140 initiatives across the continent.

Civic technologists also created a comprehensive COVID-19 data platform available at global.health, described as the first easy-to-use global repository, enabling open access to real-time data containing over 30 million anonymised cases in over 100 countries. The data curated on the site aims to help epidemiologists monitor the trajectory of the virus and track variants. A list of other corona-focused civic tech initiatives can be found here….

Restrictions put in place due to COVID-19 have positively impacted the earth’s climate, resulting in a pollution reduction, with carbon emissions falling globally. We’ve all seen the images of smog-free skies over the notoriously muggy cities across the world. According to reports, overall carbon dioxide emissions dropped by 7 percent compared to 2019. It’s argued that a more socially conscious and responsible consumer is likely to emerge post-pandemic, with a greater focus on sustainability, responsible living, and carbon footprint….(More)“

Building a Responsible Open Data Ecosystem: Mobility Data & COVID-19

Curated on Posted on by Stefaan Verhulst

Blog by Anna Livaccari: “Over the last year and a half, COVID-19 has changed the way people move, work, shop, and live. The pandemic has necessitated new data-sharing initiatives to understand new patterns of movement, analyze the spread of COVID-19, and inform research and decision-making. Earlier this year, Cuebiq collaborated with the Open Data Institute (ODI) and NYU’s The GovLab to explore the efficacy of these new initiatives. 

The ODI is a non-profit organization that brings together commercial and non-commercial organizations and governments to address global issues as well as advise on how data can be used for positive social good. As part of a larger project titled “COVID-19: Building an open and trustworthy data ecosystem,” the ODI published a new report with Cuebiq and The GovLab, an action research center at NYU’s Tandon School of Engineering that has pioneered the concept of data collaboratives and runs the data stewards network among other initiatives to advance data-driven decision making in the public interest. This report, “The Use of Mobility Data for Responding to the COVID-19 Pandemic,” specifically addresses key enablers and obstacles to the successful sharing of mobility data between public and private organizations during the pandemic….

Since early 2020, researchers and policy makers have been eager to understand the impact of COVID-19. With the help of mobility data, organizations from different sectors were able to answer some of the most pressing questions regarding the pandemic: questions about policy decisions, mass-communication strategies, and overall socioeconomic impact. Mobility data can be applied to specific use cases and can help answer complex questions, a fact that The GovLab discusses in its short-form mobility data brief. Understanding exactly how organizations employ mobility data can also improve how institutions operate post-pandemic and make data collaboration as a whole more responsible, sustainable, and systemic.

Cuebiq and the GovLab identified 51 projects where mobility data was used for pandemic response, and then selected five case studies to analyze further. The report defines mobility data, the ethics surrounding it, and the lessons learned for the future….(More)”.

The Open-Source Movement Comes to Medical Datasets

Curated on Posted on by Stefaan Verhulst

Blog by Edmund L. Andrews: “In a move to democratize research on artificial intelligence and medicine, Stanford’s Center for Artificial Intelligence in Medicine and Imaging (AIMI) is dramatically expanding what is already the world’s largest free repository of AI-ready annotated medical imaging datasets.

Artificial intelligence has become an increasingly pervasive tool for interpreting medical images, from detecting tumors in mammograms and brain scans to analyzing ultrasound videos of a person’s pumping heart.

Many AI-powered devices now rival the accuracy of human doctors. Beyond simply spotting a likely tumor or bone fracture, some systems predict the course of a patient’s illness and make recommendations.

But AI tools have to be trained on expensive datasets of images that have been meticulously annotated by human experts. Because those datasets can cost millions of dollars to acquire or create, much of the research is being funded by big corporations that don’t necessarily share their data with the public.

“What drives this technology, whether you’re a surgeon or an obstetrician, is data,” says Matthew Lungren, co-director of AIMI and an assistant professor of radiology at Stanford. “We want to double down on the idea that medical data is a public good, and that it should be open to the talents of researchers anywhere in the world.”

Launched two years ago, AIMI has already acquired annotated datasets for more than 1 million images, many of them from the Stanford University Medical Center. Researchers can download those datasets at no cost and use them to train AI models that recommend certain kinds of action.

Now, AIMI has teamed up with Microsoft’s AI for Health program to launch a new platform that will be more automated, accessible, and visible. It will be capable of hosting and organizing scores of additional images from institutions around the world. Part of the idea is to create an open and global repository. The platform will also provide a hub for sharing research, making it easier to refine different models and identify differences between population groups. The platform can even offer cloud-based computing power so researchers don’t have to worry about building local resource intensive clinical machine-learning infrastructure….(More)”.

The “Onion Model”: A Layered Approach to Documenting How the Third Wave of Open Data Can Provide Societal Value

Curated on Posted on by Stefaan Verhulst

Blog post by Andrew Zahuranec, Andrew Young and Stefaan Verhulst: “There’s a lot that goes into data-driven decision-making. Behind the datasets, platforms, and analysts is a complex series of processes that inform what kinds of insight data can produce and what kinds of ends it can achieve. These individual processes can be hard to understand when viewed together but, by separating the stages out, we can not only track how data leads to decisions but promote better and more impactful data management.

Earlier this year, The Open Data Policy Lab published the Third Wave of Open Data Toolkit to explore the elements of data re-use. At the center of this toolkit was an abstraction that we call the Open Data Framework. Divided into individual, onion-like layers, the framework shows all the processes that go into capitalizing on data in the third wave, starting with the creation of a dataset through data collaboration, creating insights, and using those insights to produce value.

This blog tries to re-iterate what’s included in each layer of this data “onion model” and demonstrate how organizations can create societal value by making their data available for re-use by other parties….(More)”.

Why I’m a proud solutionist

Curated on Posted on by Stefaan Verhulst

Blog by Jason Crawford: “Debates about technology and progress are often framed in terms of “optimism” vs. “pessimism.” For instance, Steven Pinker, Matt Ridley, Johan Norberg, Max Roser, and the late Hans Rosling have been called the “New Optimists” for their focus on the economic, scientific, and social progress of the last two centuries. Their opponents, such as David Runciman and Jason Hickel, accuse them of being blind to real problems in the world, such as poverty, and to risks of catastrophe, such as nuclear war.

Economic historian Robert Gordon calls himself “the prophet of pessimism.” His book The Rise and Fall of American Growth warned that the days of high economic growth are over for the United States and will not return. Gordon’s opponents include a group he calls the “techno-optimists,” such as Andrew McAfee and Erik Brynjolfsson, who have predicted a growth spurt in productivity from information technology.

It’s tempting to choose sides. But while it can be rational to be optimistic or pessimistic on any specific question, these terms are too imprecise to be adopted as a general intellectual identity. Those who identify as optimists can be too quick to dismiss or downplay the problems of technology, while self-styled technology pessimists or progress skeptics can be too reluctant to believe in solutions.

As we look forward to the post-pandemic recovery, once again we’re being tugged between the optimists, who highlight all the diseases that may soon be beaten through new vaccines, and the pessimists, who warn that humanity will never win the evolutionary arms race against microbes. But this represents a false choice. History provides us with powerful examples of people who were brutally honest in identifying a crisis but were equally active in seeking solutions.

At the end of the 19th century, William Crookes—physicist, chemist, and inventor of the Crookes tube (an early type of vacuum tube)—was the president of the British Association for the Advancement of Science. On September 7, 1898, he used the traditional annual address to the association to issue a dire warning.

The British Isles, he said, were at grave risk of running out of food. His reasoning was simple: the population was growing exponentially, but the amount of land under cultivation could not keep pace. The only way to continue to increase production was to improve crop yields. But the limiting factor on yields was the availability of nitrogen fertilizer, and the sources of nitrogen, such as the rock salts of the Chilean desert and the guano deposits of the Peruvian islands, were running out. His argument was detailed and comprehensive, based on figures for wheat production and land availability from every major European country and colony; he apologized in advance for boring his audience with statistics….(More)”.

In Need of Speed: Data can Accelerate Progress Towards Water and Sanitation for All

Curated on Posted on by Stefaan Verhulst

Article by Joakim Harlin et al: Even before COVID-19, the world was off-track to meet Sustainable Development Goal (SDG) 6 – ensuring water and sanitation for all by 2030.

The latest data, which is provided in seven SDG indicators reports published today by the UN-Water Integrated Monitoring Initiative for SDG 6 (IMI-SDG6), show us that 2 billion people worldwide still live without safely managed drinking water and 3.6 billion without safely managed sanitation. In addition, 2.3 billion people lack a basic handwashing facility with soap and water at home. Most wastewater is returned to nature untreated. One in five of the world’s river basins are experiencing rapid changes, such as flooding or drought with increased frequency and intensity, and 80% of wetland ecosystems are already lost….

We can only sustainably manage what we measure, and right now, there are too many gaps in the data, despite unprecedented, heroic levels of reporting during the chaos of the pandemic.

Last year, the IMI-SDG6 combined the efforts of WHO, UNICEF, UN-Habitat, UNEP, FAO, UNECE and UNESCO (as custodian agencies of the various SDG 6 global indicators) to reach out to countries with requests for data: this was our ‘2020 Data Drive.’

COVID-19 caused extreme difficulties for the SDG 6 national focal points in every country, with people forced to work from home with little equipment, few in-person consultations, and many data collection activities cancelled. Under the circumstances, the focal points made a remarkable effort. On average, UN Member States now have data on 8.2 out of 12 indicators (up from 7.0 in 2019), and the number reporting on nine or more indicators increased from 37 in 2019 to 92.

Despite this significant progress, large data gaps remain for some indicators, typically those that rely on in situ monitoring networks, such as water quality and aquifers. For example, many countries base their ambient water quality reporting on relatively few measurements; the poorest 20 countries reported on only 1,000 water bodies in total, whereas the richest 24 reported on nearly 60,000. Addressing these issues is a long-term, capital-intensive effort.

Our country monitoring focal points know better than anyone about the benefits and costs of robust water and sanitation monitoring systems, and the urgent need to establish them. We encourage high-level officials in national ministries to listen to what the focal points have to say. And, as we continue our capacity-building activities in countries, we also call on development partners to support this work. We call on academia, the private sector, and civil society to contribute to the joint effort by bringing their water and sanitation datasets to the table. …(More)”

“We do not feel safe”: A Kabul-based crisis alert app struggles to protect its own employees

Curated on Posted on by Stefaan Verhulst

Q and A with Sara Wahedi by Hajira Maryam: “Ehtesab, a Kabul-based startup, emerged out of a personal security-related incident that Sara Wahedi, a former Afghan government employee, experienced in May 2018. After witnessing a suicide bomb attack firsthand, Wahedi rushed home, where she could see militants roaming the streets from her balcony. The city was put on lockdown for 12 hours and left without electricity. No one, Wahedi said, knew when the electricity would be restored or when roads would be cleared. The authorities were of little help. 

“Since that moment, I kept pondering about the idea of accountability and information provision. I jotted down a few words in different languages for accountability, namely Dari and Pashto. That was the moment the term Ehtesab came to my mind.” 

Ehtesab means “accountability” in Dari and Pashto, and the app, formally launched in March 2020, offers streamlined security-related information, including general security updates in Kabul to its users. With real-time, crowdsourced alerts, users across the city can track bomb blasts, roadblocks, electricity outages, or other problems in locations close to them. The app, which generates push notifications about nearby security risks, is supported by 20 employees working out of the company’s Kabul office, according to Wahedi. 

Despite the company’s single-minded focus on security, the Ehtesab team was caught off-guard by the sudden collapse of the Afghan government over the weekend. “It was inevitable that there would be a significant shift in governance … but we weren’t expecting the Taliban to come in within the first eight hours of the day,” Wahedi said….(More)”.

Satellite Earth observation for sustainable rural development

Curated on Posted on by Stefaan Verhulst

A blog post by Peter Hargreaves: “…We find ourselves in a “golden age for satellite exploration”. ‘Big Data’ from satellite Earth observation – hereafter denoted ‘EO’ – could be an important part of the solution to the shortage of socioeconomic data required to inform several of the goals and targets that compose the United Nations (UN) Sustainable Development Goals (SDGs) [hyperlink]. In particular, the goals that pertain to socioeconomic and human wellbeing dimensions of development. EO data could play a significant role in producing the transparent data system necessary to achieve sustainable development….

Census and nationally representative household surveys are the medium through which most socioeconomic data are collected. It is impossible to understand socioeconomic conditions without them – I cannot stress this enough. But they have limitations, particularly in terms of cost and spatio-temporal coverage. In an ideal world, we would vastly upscale the spatial and temporal reporting of these surveys to cover more places and points in time. But this mass enumeration would be prohibitively expensive and *logistically impossible*. Imagine the quantity of data produced and the burden placed upon National Statistics Offices (NSOs) and governmental institutions? The 2030 end point for the SDGs would be upon us before much of the data was processed leaving very little time to use the outputs for policy.

This is where unconventional data enters the debate, and in this sphere – that of measuring socioeconomic conditions for development – EO data is unconventional. EO data has considerable potential to augment survey and census data for measuring rural poverty development in rural spaces, especially during intercensal periods, and where ground data are patchy, or non-existent. While on the subject, there is an important point to make: you can’t use EO to understand everything about a particular context. It does not matter how elaborate the model or the effort put in. Quite simply, EO cannot give you the full picture.

What EO *does* have is a five-decade temporal legacy (most platforms and data products are near continuous), and its broadly open access with low to negligible acquisition costs. EO data is also availabile across multiple spatial resolutions and is often easily comparable and complementary. When we say, ‘five-decade temporal legacy’, this means that there are roughly 50 years of EO data (if we use the Landsat program as an anchor). Not all EO platforms have operated across the whole timeline – Figure 1 below offers an idea of when different platforms were launched and for how long they were, or have been, operational. What’s more, data will be increasingly available and accessible, catalysed by technological innovation and investment in public and private ventures. A lot of this data is open access e.g. EO platforms operated by NASA or the ESA Copernicus programme, which include Landsat, MODIS, AVHRR, VIIRs, and the Sentinels amongst others. Meanwhile, the availability of EO data across multiple spatial resolutions enables disaggregation of data alongside survey and census data for subnational monitoring of socioeconomic conditions….(More)”.